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How to Build a Scalable Best Buy Price Tracker with Python

As an ecommerce professional, keeping tabs on competitor pricing is crucial but extremely tedious to do manually. A price tracker built in Python lets you monitor hundreds or even thousands of products automatically.

In this post, I‘ll share techniques I‘ve picked up from over 10 years of experience in data extraction and web scraping. Follow this detailed, step-by-step guide to build your own price tracker for

Here‘s what we‘ll cover:

  • Scraper best practices like proxies and user agents
  • Visualizing price history with interactive charts
  • Sending price drop alerts via SMS or email
  • Saving data to a scalable database backend
  • Containerizing the tracker as a portable Docker image
  • Architecting for high traffic and large datasets
  • Ideas for advanced features like competitor price tracking

Let‘s dive in!

Scraper Best Practices

When scraping any site, it‘s important to follow best practices to avoid detection. This ensures reliable data collection over time.

Here are a few tips:

Use Proxies

Scraping from a single IP address is a red flag for many sites. Proxies allow you to spread requests across multiple IPs to mimic organic traffic.

I recommend using a proxy service like BrightData which provides instant access to millions of residential and datacenter proxies globally.

Here‘s how to rotate proxies in Python requests using BrightData:

import requests
from brightdata.brightdata import BrightData

brightdata = BrightData(‘<your API key>‘)

proxies = brightdata.get_proxies() 

response = requests.get(‘‘, proxies=proxies)

BrightData proxies provide high bandwidth, low latency, and precise geographic targeting – perfect for large-scale scrapers.

Vary User Agents

Changing the user agent header also helps avoid pattern detection. Maintain a list of common user agent strings to randomly select from:

import random 

user_agents = [
  ‘Mozilla/5.0 (Windows NT 10.0; Win64; x64)...‘ 
  ‘Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)...‘,

user_agent = random.choice(user_agents)

headers = {
  ‘User-Agent‘: user_agent

response = requests.get(url, headers=headers)

Use Responsible Crawl Delays

Crawling too aggressively can get your IP blocked. Throttle requests by adding a randomized delay between each page scrape:

from time import sleep
from random import randint

# Delay between 2-6 seconds 
sleep(randint(2, 6))

This ensures spiders respect site resources. For large crawls, I recommend using orchestration tools like Scrapy to efficiently schedule scraping.

Visualizing Price History

Once you have price history data, visualizations can reveal trends and insights. I‘ll compare a few Python charting options:


Matplotlib is a popular Python data visualization library. It can produce basic plots like this price line chart:

import matplotlib.pyplot as plt

plt.plot(dates, prices)
plt.title(‘Product Price History‘)

Matplotlib Plot
Matplotlib works well for static visualization but lacks interactive features.


For interactive web dashboards, Bokeh is a good option. It can generate charts that let you hover and click for details:

from bokeh.plotting import figure, output_file, show

p = figure(title="Price History", x_axis_label=‘Date‘, y_axis_label=‘Price‘)

p.line(dates, prices)



This outputs an HTML dashboard like:
Bokeh Dashboard
The dashboard can then be updated programmatically with new data.


Plotly is another library for building browser-based analytics and data visualization apps.

It has options for rich interactive charts, dashboards, and even 3D plots.

Plotly integrates nicely with other Python data tools like NumPy, Pandas, and Scikit-Learn.

Here‘s an example Plotly express line chart:

import as px

fig = px.line(x=dates, y=prices)

Plotly Chart
I recommend Plotly if you need advanced analytics and visualizations beyond basic plotting. It has excellent documentation and supports open source licensing.

Sending Price Alerts

Getting notified immediately when prices change can help you capitalize on discounts. Here are a few methods for price drop alerts:

SMS Alerts

One of the quickest notifications is SMS text messages. The Twilio API makes sending texts simple:

from import Client

account_sid = ‘<your sid>‘ 
auth_token = ‘<your token>‘

client = Client(account_sid, auth_token)

message = client.messages.create(
   body=‘Price for {product_name} dropped to {new_price}!‘)

This will send you an SMS instantly whenever a price decreases.

Email Alerts

For richer notifications, emails include details without length restrictions. The smtplib module can send emails:

import smtplib

server = smtplib.SMTP(‘‘, 587)

msg = "Subject: Price Drop Alert!\n\nThe {product_name} price just dropped to {new_price}!"

server.sendmail(‘[email protected]‘, ‘[email protected]‘, msg)

This will send an email alert through Gmail. You can also integrate with services like MailGun or SendGrid.

App Alerts

For real-time monitoring, receiving alerts directly in Slack or Discord chat apps can be handy. Most chat tools have webhook integrations, making this easy to set up.

Integrating notifications with other systems allows reacting to price changes immediately.

Storing Data

For a scalable price tracker, the database becomes important as your product catalog grows. SQL and NoSQL options both have advantages:


A relational database like PostgreSQL is a robust choice. It offers excellent support for time series data with column types like TIMESTAMP:

  product_id INT, 
  price NUMERIC,
  date TIMESTAMP  

We can query prices over time for a product:

SELECT * FROM prices 
WHERE product_id = 123

PostgreSQL handles structured pricing history well. It also has enterprise features like sharding for massive datasets.


For more flexible schemas, a document store like MongoDB is great for price tracking.

Products can be stored as independent documents:

  "_id": "123",
  "product": "Laptop",  
  "prices": [
     {"date": "2022-01-01", "price": 999.99},
     {"date": "2022-01-15", "price": 899.99}

MongoDB autoscales and has visualization tools to build analytics on price data.

If storage needs are minimal, a simple CSV file can work too. As the tracker scales, migrating to a robust database ensures fast, reliable access.

Containerizing with Docker

Containerizing the price tracker as a Docker image provides portability and simplifies deployment.

Here is a sample Dockerfile:

FROM python:3.8-slim

COPY . /app 

RUN pip install -r /app/requirements.txt

CMD [ "python", "/app/" ]  

This creates an optimized Python image to run the tracker script.

With Docker, we can:

  • Version build artifacts immutably
  • Test locally then deploy identically to any environment
  • Orchestrate and scale containers across servers

I recommend Dockerizing data pipelines early on for flexibility. The tracker can then be managed through tools like Kubernetes.

Architecting for Scale

If tracking thousands of products across retailers, optimizing for scale is important.

Here are a few tips:

  • Distribute scraping across servers using frameworks like Scrapy Cloud
  • Monitor resource usage and isolate bottlenecks
  • Introduce caching and CDNs to minimize duplicate work
  • Shard databases and partition tables
  • Employ queueing to smooth out traffic spikes
  • Enforce rate limiting to avoid target sites blocking traffic

An event-driven "scraper mesh" based on tools like Kafka, Redis and Scrapy lets you handle even the largest catalogs and datasets.

If I‘m building an enterprise-grade price tracker, I leverage cloud infrastructure for auto-scaling, security and reliability.

Ideas for Advanced Features

Once your basic tracker is complete, there are plenty of cool enhancements you could add:

  • Track prices across competitors like Walmart, Target, Amazon etc.
  • Set up re-checking of prices at customizable intervals
  • Create a browser extension to track prices on any site
  • Analyze price history data for seasonality and trends
  • Implement natural language price alerts like "text me if this drops below $X"
  • Build machine learning models to forecast future price changes
  • Create mobile apps to give access to price data on the go

Let me know if you need help designing any custom integrations or analytics!

Wrap Up

I hope this guide gives you a solid foundation for building a Python price tracker, especially when scraping complex sites like Best Buy.

Optimizing your scraper, visualizing changes, sending alerts, and storing data efficiently will provide invaluable business insights.

If you have any other questions feel free to reach out! I‘m always happy to help fellow developers learn the nuances of web scraping and data pipelines.

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